metadata
license: mit
task_categories:
- tabular-regression
tags:
- biology
- genomics
pretty_name: gReLU tutorial 3 dataset (Microglia scATAC-seq)
size_categories:
- 10K<n<100K
microglia-scatac-tutorial-data
Dataset Summary
This dataset contains pseudobulk scATAC-seq data for human microglia, derived from the study by Corces et al. (2020) (https://www.nature.com/articles/s41588-020-00721-x). Genome coordinates correspond to the hg38 reference genome. This data is used in tutorial 3 of gReLU (https://github.com/Genentech/gReLU/blob/main/docs/tutorials/3_train.ipynb).
Dataset Structure
The dataset is divided into two configurations: peaks and fragments.
1. Peaks Configuration (peak_file.narrowPeak)
Standard ENCODE narrowPeak format (tab-separated).
chrom: Chromosome / Contig name.start: 0-based start position.end: End position.name: Peak identifier.score: Integer score for display.strand: Orientation.signalValue: Measurement of overall enrichment.pValue: Statistical significance (-log10).qValue: False discovery rate (-log10).peak: Point-source (summit) relative to start.
2. Fragments Configuration (fragment_file.bed)
Standard BED6 format representing individual ATAC-seq fragments.
chrom: Chromosome.start: Start position.end: End position.source: Sequencing run identifier (e.g.,SRR11442505).score: Placeholder (0).strand: Orientation.
Usage
from huggingface_hub import hf_hub_download
import grelu.io.bed
import pandas as pd
peak_path = hf_hub_download(
repo_id="Genentech/microglia-scatac-tutorial-data",
repo_type="dataset",
filename="peak_file.narrowPeak"
)
peaks = grelu.io.bed.read_narrowpeak(peak_file)
frag_path = hf_hub_download(
repo_id="Genentech/microglia-scatac-tutorial-data",
repo_type="dataset",
filename="fragment_file.bed"
)
fragments = pd.read_csv(frag_path, sep='\t', header=None,
names=['chrom', 'start', 'end', 'source', 'score', 'strand'])